Question: The optimal use of the point intercept method (PIM) for efficient estimation of plant biomass has not been addressed although PIM is a commonly used method in vegetation analysis. In this study we compare results achieved using PIM at a range of efforts, we assess a method for calculating these results that are new with PIM and we provide a formula for planning the optimal use of PIM.
Location: Northern Norway.
Methods: We collected intercept data at a range of efforts, i.e. from one to 100 pins per 0.25 m2 plots, on three plant growth forms in a mountain meadow. After collection of intercept data we clipped and weighed the plant biomass. The relationship between intercept frequency and weighed biomass (b) was estimated using both a weighted linear regression model (WLR) and an ordinary linear regression model (OLR). The accuracy of the estimate of biomass achieved by PIM at different efforts was assessed by running computer simulations at different pin densities.
Results: The relationship between intercept frequency and weighed biomass (b) was far better estimated using WLR compared to the normally used OLR. Efforts above 10 pins per 0.25 m2 plot had a negligible effect on the accuracy of the estimate of biomass achieved by PIM whereas the number of plots had a strong effect. Moreover, for a given level of accuracy, the required number of plots varied depending on plant growth form. We achieved similar results to that of the computer simulations when applying our WLR based formula.
Conclusion: This study shows that PIM can be applied more efficiently than was done in previous studies for the purpose of plant biomass estimation, where several plots should be analysed but at considerably less effort per plot. Moreover, WLR rather than OLR should be applied when estimating biomass from intercept frequency. The formula we have deduced is a useful tool for planning plant biomass analysis with PIM.
Nomenclature: Lid & Lid (1994).
Abbreviations: BM = Biomass; CV = Coefficient of variation; IF = Intercept frequency; PIM = Point intercept method; OLR = Ordinary linear regression model; WLR = weighted linear regression model.
